1 citations · 1 across the 9 of their papers we have counts for
9 papers
PDSL: Propagation Dynamics Aware Framework for Source Localization
Yansong Wang, Qisen Chai, Longlong Lin +1
Source localization is a representative inverse inference task in information propagation, aiming to identify the source node or node set that triggers the propagation results base…
Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
Zeli Wang, Qingxuan Yang, Shuyin Xia +3
Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning te…
From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering
Li Ni, Shuaikang Zeng, Lin Mu +1
Contrastive learning has demonstrated strong performance in attributed hypergraph clustering. Typically, existing methods based on contrastive learning first learn node embeddings…
NCSAC: Effective Neural Community Search via Attribute-augmented Conductance
Longlong Lin, Quanao Li, Miao Qiao +5
Identifying locally dense communities closely connected to the user-initiated query node is crucial for a wide range of applications. Existing approaches either solely depend on ru…
Effective and Efficient Conductance-based Community Search at Billion Scale
Longlong Lin, Yue He, Wei Chen +3
Community search is a widely studied semi-supervised graph clustering problem, retrieving a high-quality connected subgraph containing the user-specified query vertex. However, exi…
CoATA: Effective Co-Augmentation of Topology and Attribute for Graph Neural Networks
Tao Liu, Longlong Lin, Yunfeng Yu +4
Graph Neural Networks (GNNs) have garnered substantial attention due to their remarkable capability in learning graph representations. However, real-world graphs often exhibit subs…
Why Are Learned Indexes So Effective but Sometimes Ineffective?
Qiyu Liu, Siyuan Han, Yanlin Qi +4
Learned indexes have attracted significant research interest due to their ability to offer better space-time trade-offs compared to traditional B+-tree variants. Among various lear…
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Longlong Lin, Yunfeng Yu, Zihao Wang +4
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous d…
PSMC: Provable and Scalable Algorithms for Motif Conductance Based Graph Clustering
Longlong Lin, Tao Jia, Zeli Wang +2
Higher-order graph clustering aims to partition the graph using frequently occurring subgraphs. Motif conductance is one of the most promising higher-order graph clustering models…